Pictures are important to us; they provoke reaction, stimulate ideas, and rekindle memories. Images on the Web are no exception. However, Web images can be difficult to search and retrieve. Few studies have been conducted to identify the ‘entry points’ (Jolicoeur, Gluck,& Kosslyn, 1984), or the most commonly preferred detail level of description, used by online image searchers. Based on the idea of entry points, every object has one particular entry point at which contact is first made with semantic memory, otherwise known as the ‘basic level’ (Rosch, Mervis, Gray, Johnson,& Boyes-Braem, 1976). However, as Jolicoeur, Gluck, and Kosslyn concluded, visual objects are not always primarily identified at the basic level. Entry points may vary according to the user’s domain knowledge of the items or concepts represented in a picture. At the same time, general terms may be needed for Web use (Goodrum & Spink, 2001). Image searchers’ chosen entry points when manifested as folksonomy-based ‘tags’ (descriptive terms chosen with no restrictions placed on users’ descriptions) could potentially replace or enhance the authoritative and pre-defined lists of subject terms assigned by select experts (Neal, 2007; Neal, 2008). Tags are commonly used as descriptive indexing terms on social websites such as the photograph sharing website Flickr (http://www.flickr.com).

The act of librarians assigning subject headings or other controlled vocabulary terms is not ideal in every situation, since they choose terms that are based on their own interpretations (Krause, 1988). Greisdorf and O’Connor (2005) found that a strong personal element exists in individuals’ image descriptions. Shatford (1986) addresses the complex issue of determining the ‘ofness’ and the ‘aboutness’ present in a visual document.

The ten-level Pyramid model (Jaimes & Chang , 2000; Jorgensen, Jaimes, Benitez, & Chang, 2001) provides a guide for describing images based on four levels of visual perception (e.g., color, texture, etc.) and six levels of semantic concepts (e.g., objects, events, etc.) Within its levels, it distinguishes image content in a variety of ways, such as generic, specific, and abstract semantic content. The purpose of this study was to develop and test a new Pyramid-influenced hierarchical model for categories of image description based on participants’ descriptions of photographs.

Methodology

First, we analyzed the all-time most popular tags used on Flickr (http://www.flickr.com/photos/tags) in order to identify some predominant image tag categories. The following eight categories emerged: Color, Living Thing, Time, Events, Places, Concepts, Environment, and Objects. We then chose 16 photographs from Dr. Neal’s personal photo collection that seemed to best represent each category; two pictures per category. A total of 138 people participated in the online survey, which was distributed via SurveyMonkey. As part of the survey, participants responded to the following prompt about each image: “Provide your first, instant reaction to the content of each picture. Only spend a few seconds looking at the picture. Describe what you see in 10 words or less.” The purpose of this was to elicit participants’ preferred entry points for the 16 provided photographs.

After collecting and performing a preliminary data analysis, we created a hierarchy based on the participants’ responses to the survey. There are seven main categories in the hierarchy. Moving from the highest to the lowest level of abstraction, the categories are Abstract Scene, Abstract Object, Specific Scene, Specific Object, Generic Scene, Generic Object, and Physical Content. Six of these seven main categories have subcategories, and each category and subcategory includes a definition. Using content analysis, two coders classified each participant’s response by category and subcategory in the hierarchy. They were instructed to assign the first category in the list that they thought described the participant’s response, thus assigning the highest applicable level of abstraction. Then, we analyzed the results from the two coders, and compared the results with the Pyramid model.

Results and Discussion

At the main category level, Cohen’s kappa was 0.56. Given that values over 0.70 are considered satisfactory in most situations, the intercoder reliability level may seem slightly low, but this data was based on seven categories, unlike other studies that address fewer categories. For example, Rorissa and Iyer’s (2008) study used only three categories (subordinate, basic, and superordinate) in the coding process. Thus, a kappa value of 0.56 with seven categories may be considered as more than moderately reliable. Furthermore, only the data for which both coders were in agreement were analyzed. Additionally, the kappa rating in the current study reflects the subjective nature of image descriptions, as well as possible interpretations of those descriptions.

We defined the most frequent subcategories as the ones that the coders chose at least 150 times. Based on their codes, the ten most frequently chosen subcategories were used in 70.9% of the assigned codes. Generic Scene (Event) was the most frequently chosen (11.2%). Abstract Scene (Emotion) was the second most frequently chosen (9.8%). The other top choices, in order of next most frequently chosen, were Generic Object (Artificial Object), (9.4%), Generic Scene (Location with Specific Information) (69%), Generic Scene (Nature Scene) (6.8%), Generic Object (Human with Location Information) (5.7%), Abstract Scene (Opinion) (5.6%), Generic Object (Artificial Object with Color Description) (5.5%), Abstract Scene (Talk Bubbles) (5.2%), and Generic Scene (Location) (4.9%).

Based on the coding and analysis, participants only selected entry points that fall under three of the seven main categories: Generic Scene, Generic Object, and Abstract Scene. Five of the image types (Event, Object, Living Thing, Places, and Time) were coded under Abstract Scene. Among the 10 levels in the Pyramid model, only Generic Object, Abstract Scene, and Generic Scene are used in the participants’ descriptions. Also related to the Pyramid model, the various image types frequently belong to the same main categories and subcategories. For example, the most frequent subcategory for Color is Generic Scene (Nature Scene) and the one for Concept is Generic Scene (Event). It seems necessary to subdivide the Pyramid model into subcategories in order to more accurately reflect the preferred entry points, especially for the most frequently used categories.

Between the coders, there was not a single agreement of the most frequent subcategory for any two images under the same Flickr tag-based category such as Color or Time in the results. The coders did not agree on any subcategory for the image pairs based on the categories we created from the Flickr tags. In many cases, when there were conflicting results between two images that fell under the same Flickr tag-based category, the most frequent category chosen from our hierarchy was Generic Object. While the authors’ process of categorizing the Flickr tags was a subjective process in itself, the results indicate the challenge in achieving interindexer consistency.

Also, our results indicate that neither the size nor the location of the object was the main factor in the participants’ descriptions, but the context around the object is a powerful factor. For example, some images show that small artificial objects are noticed often, and the image is categorized accordingly. For example, Event (‘Birthday’) and Environment (‘Sunshine over a statue’) were coded as Generic Object. Thus, our results illustrate the need for further investigation regarding what factors influence the user’s decision on whether the image is about a Generic Object.

Our findings in this study emphasize the importance of a ‘bottom-up’ approach for Web image descriptions in situations where it is impossible for a few experts to describe numerous images. We believe that user-based techniques such as tagging must be actively used in indexing Web images. The findings in this study will be used for continued research in the area of Web 2.0-based image retrieval, topical relevance in image data, and novel interface designs for image retrieval systems.

http://tinyurl.com/buq5k8b Mark White wonders: ” When we look at pictorial space, at traditional paintings in museums, what are we looking at? Their original context was so different, what draws us in now? They are clearly very popular. Most of these posts are written in the National Gallery in London, it is always packed; what are we all seeing? How do we approach the illusion of pictorial space now?”

This project will analyze how users of an online image collection site (Pinterest.com) organize their digital images to determine howPanofsky’s concept of three levels of visual understanding translates to an online image collection

How do Pinterest users apply Panofsky’s concept of iconology to their online collections of images? Does the way Pintetest users organize their images differ from the way iconology concepts are applied to static nondigital image collections?

The purpose of this study is to analyze the way Pinterest users organize their image collections using Panofsky’s three levels of understanding (pre-icongraphc, iconographic and iconolgy) and to determine in what ways the organizational systems of users of online image collections differs from organizational methods of users of nondigital image collections.

A cross section of random Pinterest boards will be collected and analyzed using content analysis. The goal is to determine if/how the user organization schemes found to be most common on Pinterest in 2012 correspond to Panofsky’s three levels of understanding.

What differences exist between the way Pinterest users organize their online image collections and the way non-digital image collectors organize their collections?

Using Panofsky’s three levels of understanding (pre-icongraphc, iconographic and iconolgy) as a lens, in what ways do the organizational systems of users of online image collections differ from users of nondigital image collections?

Greisdorf and O’Connor (2008):Structures of Image Collections

Panofsky, Erwin. (1972.) Studies in Iconology: Humanistic Themes in the Art of the Renaissance. New York: Harper & Row

Tuominen, K. & Savolainen, R (1997). A social constructionist approach to the study of information use as discursive action. In P.Vakkari, R. Savolainen, & B. Dervin (Eds.). Information seeking in context. Proceedings of an international conference on research in information needs, seeking and use in different contexts, 14-16 August 1996, Tampere, Finland. (pp. 81-96). London: Taylor Graham.

“At MiT5, the most recent in MIT’s series of Media in Transition conferences, Dr. Thomas Pettitt of the University of Southern Denmark theorized that participatory cultures signal the imminent closing of what he called “the Guttenberg parenthesis.”

According to that theory, we are witnessing a return to the cultural norms that prevailed before the advent of mechanically printed texts. In those oral and folk cultures, practices such as adaptation, appropriation and recombining – what hip-hop calls “sampling” – were not only accepted but lauded. The printing press brought a new regime, in which individual printed works were held to be utterly unique and an author’s ownership sacrosanct. But now, according to theorists such as Dr. Pettitt, new digital technologies are spinning the wheel back to its pre-Guttenberg position (closing the parenthesis), and cultural production will be understood once again as a dynamic, collective process rather than the work of a single lonely genius, laboring alone in a garret.” — from “Harry Potter Obsession” by Victoria Loe Hicks in the Dallas Morning News July 8 2007.

But what’s truly jaw-dropping is the sheer mass and variety of fan-generated content: essays and forums analyzing the books’ most minute details, endless plot speculation, multiple genres of fanfic (fan-written fiction employing Ms. Rowling’s characters), fan art, manips (manipulations of images via Photoshop and similar programs), music videos that combine photos and film clips into original narratives accompanied by appropriate pop tunes, filks (humorous song lyrics about the characters, set to pop tunes) and original wizard rock (yes, according to http://www.wizrocklepdia.com, there are more than 200 wizard rock bands, from Aberforth Dumbledore and the Nannies to the Wonky Cross).

With the exception of wizard rock, most types of content – fanfic or filks, for instance – are staples of late 20th-century fan culture, dating back at least as far as the original Star Trek. But Dr. Jenkins, who has parsed the meaning of the digitally enhanced Potter fandom more closely than any other scholar, sees in it a prime example of an emerging “participatory culture.” In such a culture, Dr. Jenkins blogged, “what might traditionally be understood as media producers and consumers are transformed into participants who are expected to interact with each other according to a new set of rules which none of us fully understands.” — from “Harry Potter Obsession” by Victoria Loe Hicks in the Dallas Morning News July 8 2007.

Is visual art a specialized kind of information,
which can be manipulated using the tools of formation science?
What are the defining informational characteristics
of visual art, if this is the case?

Or does visual information act as the raw material of art, the essence which combines with media and action to yeild expression? Do the traditional divisions of information as a raw material support this idea of art-making?

How often does a piece of pure information become valued as art?
What are the steps leading to this valuation?
How would we define “pure”?

“The facts are dead, long live the facts. It seems simple enough: The job of science is to observe, describe, and explain the natural world through hypothesis and experimentation. A scientist will say, “I think this explanation is the reason for this observation, and I propose this experiment to test it.” But the statement doesn’t begin to convey the job at hand. Theories, hypotheses, laws, the scientific method — even facts themselves — dangle from the natural sciences like so many tree branches. How do the various parts fit together?”

Abstract

This study examines existent and new methods for evaluating the success of information retrieval systems. The theory underlying current methods is not robust enough to allow testing retrieval using different meta-tagging schemas. Traditional measures rely on judgments of whether a document is relevant to a particular question. A good system returns all the relevant documents and no extraneous documents. There is a rich literature questioning the efficacy of relevance judgments. Such questions as, Relevant to whom? When? and To what purpose? are not well-answered in traditional theory. In this study, two new measures (Spink’s Information Need and Cooper’s Utility) are used in evaluating two search tools (tag-based and text-based), comparing these new measures with traditional measures and each other. The open-source Swish text-based search engine and a self-constructed tag-based search tool were used. Thirty-four educators searched for information using both search engines and evaluated the information retrieved by each. Construct measures, derived by multiplying each of the three measures (traditional, information need, and utility) by a rating of satisfaction were compared using two way analysis of variance. This study specifically analyzes small information systems. The design concepts would be untenable for large systems. Results indicated that there was a significant correlation between the three measures, indicating that the new measures provide an equivalent method of evaluating systems and have some significant advantages, which include not requiring relevance judgments and the ability to use the measures in situ.

How do scientific visual images which are not generally considered fine art (DNA trees, crystal diagrams, sonar charts) sometimes acquire aesthetic value outside of their specific scientific community?In particular, what happens when online scientific information changes into fine art? What are the economic, political and aesthetic forces behind such a change? When does this redefinition occur? What are the distinctive components of art and information in this case? What is the history of informational images acquiring aesthetic value?

The infinite variety of misinformation, from mild interpretation errors to intentional lies, forms a measurable layer of the information environment. How can we classify and describe the numerous varieties of information that are not correct?

Obviously, conscious intent to deceive is a baseline variable, but- what else?
If “social constructionism” is the process used to uncover the ways in which individuals and groups participate in the creation of their perceived reality, including the ways social phenomena are created, institutionalized, and made into tradition by humans, what is the name of the process which occurs when the perceived reality does not mesh with the facts?

The circuitry of primary visual cortex organizes the visual scene encoded by the retinae into spatially organized “maps” of features: a map of visual space, a map of ocular dominance (eye-specific information), a map of the orientations (angles) of lines…

er all, is writing which can be read nonsequentially, in an infinite variety of ways. And as Derrida suggests, this type of writing implies a new idea of space. text, after all, is writing which can be read nonsequentially, in an infinite variety of ways. And as Derrida suggests, this type of writing implies a new idea of space.

RDF and OWL are Semantic Web standards that provide a framework for asset management, enterprise integration and the sharing and reuse of data on the Web. A. OWL is a Web Ontology language. Where earlier languages have been used to develop tools and ontologies for specific user communities (particularly in the sciences and in company-specific e-commerce applications), they were not defined to be compatible with the architecture of the World Wide Web in general, and the Semantic Web in particular.